Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations20592
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.7 MiB
Average record size in memory646.6 B

Variable types

Text5
Categorical2
Numeric15
Boolean2

Alerts

Licensed has constant value "True" Constant
official_video has constant value "True" Constant
Acousticness is highly overall correlated with EnergyHigh correlation
Comments is highly overall correlated with Likes and 2 other fieldsHigh correlation
Energy is highly overall correlated with Acousticness and 1 other fieldsHigh correlation
EnergyLiveness is highly overall correlated with LivenessHigh correlation
Likes is highly overall correlated with Comments and 2 other fieldsHigh correlation
Liveness is highly overall correlated with EnergyLivenessHigh correlation
Loudness is highly overall correlated with EnergyHigh correlation
Stream is highly overall correlated with Comments and 2 other fieldsHigh correlation
Views is highly overall correlated with Comments and 2 other fieldsHigh correlation
Duration_min is highly skewed (γ1 = 23.34614694) Skewed
Comments is highly skewed (γ1 = 44.15779383) Skewed
Instrumentalness has 9317 (45.2%) zeros Zeros
Views has 469 (2.3%) zeros Zeros
Likes has 556 (2.7%) zeros Zeros
Comments has 1064 (5.2%) zeros Zeros
Stream has 576 (2.8%) zeros Zeros

Reproduction

Analysis started2025-03-10 22:13:13.614611
Analysis finished2025-03-10 22:13:53.449978
Duration39.84 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Artist
Text

Distinct2074
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2025-03-11T03:43:53.804923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length45
Median length32
Mean length10.987131
Min length2

Characters and Unicode

Total characters226247
Distinct characters105
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowGorillaz
2nd rowGorillaz
3rd rowGorillaz
4th rowGorillaz
5th rowGorillaz
ValueCountFrequency (%)
the 840
 
2.2%
439
 
1.1%
los 290
 
0.7%
de 200
 
0.5%
la 150
 
0.4%
el 130
 
0.3%
james 130
 
0.3%
of 124
 
0.3%
john 120
 
0.3%
lil 119
 
0.3%
Other values (2954) 36456
93.5%
2025-03-11T03:43:54.378193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 20351
 
9.0%
e 19197
 
8.5%
18406
 
8.1%
i 13744
 
6.1%
o 13309
 
5.9%
n 12908
 
5.7%
r 12341
 
5.5%
l 9548
 
4.2%
s 8984
 
4.0%
t 7410
 
3.3%
Other values (95) 90049
39.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 161501
71.4%
Uppercase Letter 43396
 
19.2%
Space Separator 18406
 
8.1%
Other Punctuation 1545
 
0.7%
Decimal Number 919
 
0.4%
Dash Punctuation 330
 
0.1%
Currency Symbol 110
 
< 0.1%
Math Symbol 20
 
< 0.1%
Open Punctuation 10
 
< 0.1%
Close Punctuation 10
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 20351
12.6%
e 19197
11.9%
i 13744
 
8.5%
o 13309
 
8.2%
n 12908
 
8.0%
r 12341
 
7.6%
l 9548
 
5.9%
s 8984
 
5.6%
t 7410
 
4.6%
h 6022
 
3.7%
Other values (34) 37687
23.3%
Uppercase Letter
ValueCountFrequency (%)
S 3489
 
8.0%
M 3204
 
7.4%
C 2960
 
6.8%
T 2819
 
6.5%
B 2751
 
6.3%
A 2705
 
6.2%
L 2503
 
5.8%
D 2349
 
5.4%
R 2218
 
5.1%
J 2034
 
4.7%
Other values (24) 16364
37.7%
Other Punctuation
ValueCountFrequency (%)
. 717
46.4%
& 409
26.5%
' 149
 
9.6%
! 70
 
4.5%
" 60
 
3.9%
? 50
 
3.2%
, 40
 
2.6%
/ 20
 
1.3%
* 20
 
1.3%
: 10
 
0.6%
Decimal Number
ValueCountFrequency (%)
1 220
23.9%
2 170
18.5%
5 119
12.9%
4 100
10.9%
0 80
 
8.7%
7 60
 
6.5%
6 50
 
5.4%
3 50
 
5.4%
9 40
 
4.4%
8 30
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 310
93.9%
20
 
6.1%
Space Separator
ValueCountFrequency (%)
18406
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 110
100.0%
Math Symbol
ValueCountFrequency (%)
+ 20
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 204897
90.6%
Common 21350
 
9.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 20351
 
9.9%
e 19197
 
9.4%
i 13744
 
6.7%
o 13309
 
6.5%
n 12908
 
6.3%
r 12341
 
6.0%
l 9548
 
4.7%
s 8984
 
4.4%
t 7410
 
3.6%
h 6022
 
2.9%
Other values (68) 81083
39.6%
Common
ValueCountFrequency (%)
18406
86.2%
. 717
 
3.4%
& 409
 
1.9%
- 310
 
1.5%
1 220
 
1.0%
2 170
 
0.8%
' 149
 
0.7%
5 119
 
0.6%
$ 110
 
0.5%
4 100
 
0.5%
Other values (17) 640
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 224809
99.4%
None 1418
 
0.6%
Punctuation 20
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 20351
 
9.1%
e 19197
 
8.5%
18406
 
8.2%
i 13744
 
6.1%
o 13309
 
5.9%
n 12908
 
5.7%
r 12341
 
5.5%
l 9548
 
4.2%
s 8984
 
4.0%
t 7410
 
3.3%
Other values (68) 88611
39.4%
None
ValueCountFrequency (%)
é 320
22.6%
á 190
13.4%
í 190
13.4%
ó 130
9.2%
ü 80
 
5.6%
ã 80
 
5.6%
ñ 60
 
4.2%
ö 60
 
4.2%
ç 49
 
3.5%
Á 30
 
2.1%
Other values (16) 229
16.1%
Punctuation
ValueCountFrequency (%)
20
100.0%

Track
Text

Distinct17715
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2025-03-11T03:43:54.825162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length195
Median length112
Mean length19.510829
Min length1

Characters and Unicode

Total characters401767
Distinct characters143
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15588 ?
Unique (%)75.7%

Sample

1st rowFeel Good Inc.
2nd rowRhinestone Eyes
3rd rowNew Gold (feat. Tame Impala and Bootie Brown)
4th rowOn Melancholy Hill
5th rowClint Eastwood
ValueCountFrequency (%)
3721
 
4.9%
feat 1742
 
2.3%
the 1628
 
2.1%
you 912
 
1.2%
me 903
 
1.2%
a 767
 
1.0%
i 704
 
0.9%
love 644
 
0.8%
of 590
 
0.8%
remix 587
 
0.8%
Other values (14881) 64259
84.0%
2025-03-11T03:43:55.502159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
55865
 
13.9%
e 34537
 
8.6%
a 27645
 
6.9%
o 23621
 
5.9%
i 19956
 
5.0%
n 17578
 
4.4%
r 16893
 
4.2%
t 16119
 
4.0%
s 12331
 
3.1%
l 12082
 
3.0%
Other values (133) 165140
41.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 250920
62.5%
Uppercase Letter 71357
 
17.8%
Space Separator 55865
 
13.9%
Other Punctuation 8365
 
2.1%
Decimal Number 5079
 
1.3%
Close Punctuation 3450
 
0.9%
Open Punctuation 3448
 
0.9%
Dash Punctuation 3032
 
0.8%
Final Punctuation 102
 
< 0.1%
Math Symbol 62
 
< 0.1%
Other values (5) 87
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 34537
13.8%
a 27645
11.0%
o 23621
 
9.4%
i 19956
 
8.0%
n 17578
 
7.0%
r 16893
 
6.7%
t 16119
 
6.4%
s 12331
 
4.9%
l 12082
 
4.8%
u 9000
 
3.6%
Other values (43) 61158
24.4%
Uppercase Letter
ValueCountFrequency (%)
S 5502
 
7.7%
T 5427
 
7.6%
M 5186
 
7.3%
A 5037
 
7.1%
L 4185
 
5.9%
D 3646
 
5.1%
R 3575
 
5.0%
B 3563
 
5.0%
C 3458
 
4.8%
I 3220
 
4.5%
Other values (32) 28558
40.0%
Other Punctuation
ValueCountFrequency (%)
. 2936
35.1%
' 1387
16.6%
, 1142
 
13.7%
" 920
 
11.0%
& 728
 
8.7%
: 418
 
5.0%
? 316
 
3.8%
/ 236
 
2.8%
! 143
 
1.7%
* 41
 
0.5%
Other values (7) 98
 
1.2%
Decimal Number
ValueCountFrequency (%)
2 1183
23.3%
0 1072
21.1%
1 1004
19.8%
5 333
 
6.6%
3 318
 
6.3%
9 281
 
5.5%
4 253
 
5.0%
7 229
 
4.5%
6 217
 
4.3%
8 189
 
3.7%
Math Symbol
ValueCountFrequency (%)
+ 37
59.7%
| 22
35.5%
= 2
 
3.2%
< 1
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 3024
99.7%
7
 
0.2%
1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 3352
97.2%
] 98
 
2.8%
Open Punctuation
ValueCountFrequency (%)
( 3350
97.2%
[ 98
 
2.8%
Final Punctuation
ValueCountFrequency (%)
89
87.3%
13
 
12.7%
Initial Punctuation
ValueCountFrequency (%)
11
91.7%
1
 
8.3%
Other Symbol
ValueCountFrequency (%)
4
80.0%
® 1
 
20.0%
Space Separator
ValueCountFrequency (%)
55865
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 60
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 322277
80.2%
Common 79490
 
19.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 34537
 
10.7%
a 27645
 
8.6%
o 23621
 
7.3%
i 19956
 
6.2%
n 17578
 
5.5%
r 16893
 
5.2%
t 16119
 
5.0%
s 12331
 
3.8%
l 12082
 
3.7%
u 9000
 
2.8%
Other values (85) 132515
41.1%
Common
ValueCountFrequency (%)
55865
70.3%
) 3352
 
4.2%
( 3350
 
4.2%
- 3024
 
3.8%
. 2936
 
3.7%
' 1387
 
1.7%
2 1183
 
1.5%
, 1142
 
1.4%
0 1072
 
1.3%
1 1004
 
1.3%
Other values (38) 5175
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 399401
99.4%
None 2238
 
0.6%
Punctuation 124
 
< 0.1%
Letterlike Symbols 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
55865
 
14.0%
e 34537
 
8.6%
a 27645
 
6.9%
o 23621
 
5.9%
i 19956
 
5.0%
n 17578
 
4.4%
r 16893
 
4.2%
t 16119
 
4.0%
s 12331
 
3.1%
l 12082
 
3.0%
Other values (78) 162774
40.8%
None
ValueCountFrequency (%)
é 371
16.6%
ó 319
14.3%
á 269
12.0%
í 259
11.6%
ú 148
 
6.6%
ã 147
 
6.6%
ñ 121
 
5.4%
ç 101
 
4.5%
ü 66
 
2.9%
É 48
 
2.1%
Other values (37) 389
17.4%
Punctuation
ValueCountFrequency (%)
89
71.8%
13
 
10.5%
11
 
8.9%
7
 
5.6%
2
 
1.6%
1
 
0.8%
1
 
0.8%
Letterlike Symbols
ValueCountFrequency (%)
4
100.0%

Album
Text

Distinct11853
Distinct (%)57.6%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2025-03-11T03:43:55.966035image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length195
Median length103
Mean length20.402438
Min length1

Characters and Unicode

Total characters420127
Distinct characters145
Distinct categories16 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7462 ?
Unique (%)36.2%

Sample

1st rowDemon Days
2nd rowPlastic Beach
3rd rowNew Gold (feat. Tame Impala and Bootie Brown)
4th rowPlastic Beach
5th rowGorillaz
ValueCountFrequency (%)
the 2686
 
3.7%
1520
 
2.1%
edition 850
 
1.2%
deluxe 823
 
1.1%
of 816
 
1.1%
original 731
 
1.0%
soundtrack 715
 
1.0%
motion 687
 
1.0%
picture 671
 
0.9%
a 600
 
0.8%
Other values (11808) 61688
85.9%
2025-03-11T03:43:56.655882image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
51195
 
12.2%
e 35041
 
8.3%
a 26082
 
6.2%
o 24691
 
5.9%
i 23835
 
5.7%
n 20552
 
4.9%
r 19183
 
4.6%
t 17417
 
4.1%
s 14293
 
3.4%
l 13762
 
3.3%
Other values (135) 174076
41.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 269381
64.1%
Uppercase Letter 76448
 
18.2%
Space Separator 51195
 
12.2%
Other Punctuation 6746
 
1.6%
Decimal Number 5398
 
1.3%
Close Punctuation 4857
 
1.2%
Open Punctuation 4857
 
1.2%
Dash Punctuation 932
 
0.2%
Math Symbol 134
 
< 0.1%
Currency Symbol 70
 
< 0.1%
Other values (6) 109
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 35041
13.0%
a 26082
9.7%
o 24691
 
9.2%
i 23835
 
8.8%
n 20552
 
7.6%
r 19183
 
7.1%
t 17417
 
6.5%
s 14293
 
5.3%
l 13762
 
5.1%
u 10390
 
3.9%
Other values (40) 64135
23.8%
Uppercase Letter
ValueCountFrequency (%)
S 6456
 
8.4%
T 6072
 
7.9%
A 5481
 
7.2%
M 5128
 
6.7%
E 4936
 
6.5%
D 4301
 
5.6%
L 3862
 
5.1%
R 3684
 
4.8%
C 3645
 
4.8%
O 3601
 
4.7%
Other values (30) 29282
38.3%
Other Punctuation
ValueCountFrequency (%)
. 2025
30.0%
: 930
13.8%
' 896
13.3%
, 763
 
11.3%
& 644
 
9.5%
" 505
 
7.5%
? 302
 
4.5%
/ 235
 
3.5%
! 214
 
3.2%
# 65
 
1.0%
Other values (8) 167
 
2.5%
Decimal Number
ValueCountFrequency (%)
2 1340
24.8%
1 1046
19.4%
0 1010
18.7%
3 474
 
8.8%
5 364
 
6.7%
9 317
 
5.9%
4 281
 
5.2%
8 192
 
3.6%
6 190
 
3.5%
7 184
 
3.4%
Math Symbol
ValueCountFrequency (%)
+ 84
62.7%
| 21
 
15.7%
~ 16
 
11.9%
= 6
 
4.5%
< 3
 
2.2%
÷ 3
 
2.2%
× 1
 
0.7%
Dash Punctuation
ValueCountFrequency (%)
- 917
98.4%
14
 
1.5%
1
 
0.1%
Other Symbol
ValueCountFrequency (%)
° 6
50.0%
4
33.3%
® 2
 
16.7%
Close Punctuation
ValueCountFrequency (%)
) 4596
94.6%
] 261
 
5.4%
Open Punctuation
ValueCountFrequency (%)
( 4596
94.6%
[ 261
 
5.4%
Final Punctuation
ValueCountFrequency (%)
54
94.7%
3
 
5.3%
Initial Punctuation
ValueCountFrequency (%)
5
62.5%
3
37.5%
Modifier Symbol
ValueCountFrequency (%)
´ 1
50.0%
` 1
50.0%
Space Separator
ValueCountFrequency (%)
51195
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 70
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 28
100.0%
Other Letter
ValueCountFrequency (%)
º 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 345831
82.3%
Common 74296
 
17.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 35041
 
10.1%
a 26082
 
7.5%
o 24691
 
7.1%
i 23835
 
6.9%
n 20552
 
5.9%
r 19183
 
5.5%
t 17417
 
5.0%
s 14293
 
4.1%
l 13762
 
4.0%
u 10390
 
3.0%
Other values (81) 140585
40.7%
Common
ValueCountFrequency (%)
51195
68.9%
) 4596
 
6.2%
( 4596
 
6.2%
. 2025
 
2.7%
2 1340
 
1.8%
1 1046
 
1.4%
0 1010
 
1.4%
: 930
 
1.3%
- 917
 
1.2%
' 896
 
1.2%
Other values (44) 5745
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 418120
99.5%
None 1916
 
0.5%
Punctuation 87
 
< 0.1%
Letterlike Symbols 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
51195
 
12.2%
e 35041
 
8.4%
a 26082
 
6.2%
o 24691
 
5.9%
i 23835
 
5.7%
n 20552
 
4.9%
r 19183
 
4.6%
t 17417
 
4.2%
s 14293
 
3.4%
l 13762
 
3.3%
Other values (81) 172069
41.2%
None
ValueCountFrequency (%)
ó 295
15.4%
é 288
15.0%
í 194
10.1%
á 178
9.3%
ú 128
 
6.7%
ñ 124
 
6.5%
ü 117
 
6.1%
ã 105
 
5.5%
ç 72
 
3.8%
É 53
 
2.8%
Other values (36) 362
18.9%
Punctuation
ValueCountFrequency (%)
54
62.1%
14
 
16.1%
7
 
8.0%
5
 
5.7%
3
 
3.4%
3
 
3.4%
1
 
1.1%
Letterlike Symbols
ValueCountFrequency (%)
4
100.0%

Album_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
album
14832 
single
4973 
compilation
 
787

Length

Max length11
Median length5
Mean length5.4708139
Min length5

Characters and Unicode

Total characters112655
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowalbum
2nd rowalbum
3rd rowsingle
4th rowalbum
5th rowalbum

Common Values

ValueCountFrequency (%)
album 14832
72.0%
single 4973
 
24.2%
compilation 787
 
3.8%

Length

2025-03-11T03:43:56.872320image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T03:43:57.033486image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
album 14832
72.0%
single 4973
 
24.2%
compilation 787
 
3.8%

Most occurring characters

ValueCountFrequency (%)
l 20592
18.3%
a 15619
13.9%
m 15619
13.9%
b 14832
13.2%
u 14832
13.2%
i 6547
 
5.8%
n 5760
 
5.1%
s 4973
 
4.4%
g 4973
 
4.4%
e 4973
 
4.4%
Other values (4) 3935
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 112655
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 20592
18.3%
a 15619
13.9%
m 15619
13.9%
b 14832
13.2%
u 14832
13.2%
i 6547
 
5.8%
n 5760
 
5.1%
s 4973
 
4.4%
g 4973
 
4.4%
e 4973
 
4.4%
Other values (4) 3935
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 112655
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 20592
18.3%
a 15619
13.9%
m 15619
13.9%
b 14832
13.2%
u 14832
13.2%
i 6547
 
5.8%
n 5760
 
5.1%
s 4973
 
4.4%
g 4973
 
4.4%
e 4973
 
4.4%
Other values (4) 3935
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 112655
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 20592
18.3%
a 15619
13.9%
m 15619
13.9%
b 14832
13.2%
u 14832
13.2%
i 6547
 
5.8%
n 5760
 
5.1%
s 4973
 
4.4%
g 4973
 
4.4%
e 4973
 
4.4%
Other values (4) 3935
 
3.5%

Danceability
Real number (ℝ)

Distinct898
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62016204
Minimum0
Maximum0.975
Zeros17
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size837.8 KiB
2025-03-11T03:43:57.225603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.31855
Q10.519
median0.638
Q30.741
95-th percentile0.861
Maximum0.975
Range0.975
Interquartile range (IQR)0.222

Descriptive statistics

Standard deviation0.16539933
Coefficient of variation (CV)0.26670341
Kurtosis0.14162235
Mean0.62016204
Median Absolute Deviation (MAD)0.11
Skewness-0.55423341
Sum12770.377
Variance0.027356938
MonotonicityNot monotonic
2025-03-11T03:43:57.401464image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.687 78
 
0.4%
0.671 74
 
0.4%
0.626 69
 
0.3%
0.647 67
 
0.3%
0.585 66
 
0.3%
0.682 66
 
0.3%
0.673 64
 
0.3%
0.681 64
 
0.3%
0.638 63
 
0.3%
0.646 62
 
0.3%
Other values (888) 19919
96.7%
ValueCountFrequency (%)
0 17
0.1%
0.0532 1
 
< 0.1%
0.0619 1
 
< 0.1%
0.0623 1
 
< 0.1%
0.064 1
 
< 0.1%
0.0649 1
 
< 0.1%
0.065 1
 
< 0.1%
0.0673 1
 
< 0.1%
0.0686 1
 
< 0.1%
0.069 1
 
< 0.1%
ValueCountFrequency (%)
0.975 3
< 0.1%
0.973 1
 
< 0.1%
0.971 2
< 0.1%
0.97 4
< 0.1%
0.969 1
 
< 0.1%
0.968 1
 
< 0.1%
0.967 3
< 0.1%
0.966 1
 
< 0.1%
0.965 2
< 0.1%
0.964 4
< 0.1%

Energy
Real number (ℝ)

High correlation 

Distinct1267
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63523776
Minimum2.03 × 10-5
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size837.8 KiB
2025-03-11T03:43:57.582339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2.03 × 10-5
5-th percentile0.219
Q10.507
median0.666
Q30.798
95-th percentile0.929
Maximum1
Range0.9999797
Interquartile range (IQR)0.291

Descriptive statistics

Standard deviation0.21419319
Coefficient of variation (CV)0.33718585
Kurtosis0.14246736
Mean0.63523776
Median Absolute Deviation (MAD)0.143
Skewness-0.71680782
Sum13080.816
Variance0.045878721
MonotonicityNot monotonic
2025-03-11T03:43:57.792368image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.572 59
 
0.3%
0.711 57
 
0.3%
0.72 57
 
0.3%
0.785 56
 
0.3%
0.768 56
 
0.3%
0.674 55
 
0.3%
0.782 54
 
0.3%
0.703 54
 
0.3%
0.662 53
 
0.3%
0.745 53
 
0.3%
Other values (1257) 20038
97.3%
ValueCountFrequency (%)
2.03 × 10-51
 
< 0.1%
5.5 × 10-52
< 0.1%
0.000252 2
< 0.1%
0.00125 3
< 0.1%
0.00144 1
 
< 0.1%
0.00174 1
 
< 0.1%
0.00189 1
 
< 0.1%
0.00194 1
 
< 0.1%
0.00199 1
 
< 0.1%
0.00212 1
 
< 0.1%
ValueCountFrequency (%)
1 6
< 0.1%
0.999 1
 
< 0.1%
0.998 5
< 0.1%
0.997 3
 
< 0.1%
0.996 7
< 0.1%
0.995 10
< 0.1%
0.994 10
< 0.1%
0.993 5
< 0.1%
0.992 5
< 0.1%
0.991 8
< 0.1%

Loudness
Real number (ℝ)

High correlation 

Distinct9387
Distinct (%)45.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.6789994
Minimum-46.251
Maximum0.92
Zeros0
Zeros (%)0.0%
Negative20586
Negative (%)> 99.9%
Memory size837.8 KiB
2025-03-11T03:43:57.991290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-46.251
5-th percentile-15.92545
Q1-8.86825
median-6.541
Q3-4.935
95-th percentile-3.205
Maximum0.92
Range47.171
Interquartile range (IQR)3.93325

Descriptive statistics

Standard deviation4.6390889
Coefficient of variation (CV)-0.60412674
Kurtosis10.720489
Mean-7.6789994
Median Absolute Deviation (MAD)1.834
Skewness-2.7010723
Sum-158125.96
Variance21.521146
MonotonicityNot monotonic
2025-03-11T03:43:58.191376image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-7.818 25
 
0.1%
-7.768 21
 
0.1%
-6.887 15
 
0.1%
-4.501 15
 
0.1%
-6.253 14
 
0.1%
-6.246 12
 
0.1%
-5.077 12
 
0.1%
-5.76 12
 
0.1%
-6.518 11
 
0.1%
-5.549 11
 
0.1%
Other values (9377) 20444
99.3%
ValueCountFrequency (%)
-46.251 1
< 0.1%
-44.761 1
< 0.1%
-43.988 1
< 0.1%
-41.932 1
< 0.1%
-41.766 1
< 0.1%
-41.696 1
< 0.1%
-41.53 2
< 0.1%
-41.001 1
< 0.1%
-39.919 1
< 0.1%
-39.869 1
< 0.1%
ValueCountFrequency (%)
0.92 1
 
< 0.1%
0.829 1
 
< 0.1%
0.561 1
 
< 0.1%
0.522 1
 
< 0.1%
0.175 1
 
< 0.1%
0.006 1
 
< 0.1%
-0.007 1
 
< 0.1%
-0.14 1
 
< 0.1%
-0.142 1
 
< 0.1%
-0.155 4
< 0.1%

Speechiness
Real number (ℝ)

Distinct1303
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.096742444
Minimum0
Maximum0.964
Zeros17
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size837.8 KiB
2025-03-11T03:43:58.389389image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0278
Q10.0357
median0.0507
Q30.104
95-th percentile0.324
Maximum0.964
Range0.964
Interquartile range (IQR)0.0683

Descriptive statistics

Standard deviation0.1121834
Coefficient of variation (CV)1.1596089
Kurtosis16.425084
Mean0.096742444
Median Absolute Deviation (MAD)0.0194
Skewness3.3661238
Sum1992.1204
Variance0.012585116
MonotonicityNot monotonic
2025-03-11T03:43:58.573844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0324 71
 
0.3%
0.0305 66
 
0.3%
0.0317 65
 
0.3%
0.0293 64
 
0.3%
0.0288 64
 
0.3%
0.0326 64
 
0.3%
0.0377 63
 
0.3%
0.0351 62
 
0.3%
0.0315 62
 
0.3%
0.0308 62
 
0.3%
Other values (1293) 19949
96.9%
ValueCountFrequency (%)
0 17
0.1%
0.022 1
 
< 0.1%
0.0222 1
 
< 0.1%
0.0224 1
 
< 0.1%
0.0225 1
 
< 0.1%
0.0226 1
 
< 0.1%
0.0227 1
 
< 0.1%
0.0229 1
 
< 0.1%
0.023 2
 
< 0.1%
0.0231 1
 
< 0.1%
ValueCountFrequency (%)
0.964 1
< 0.1%
0.962 1
< 0.1%
0.961 2
< 0.1%
0.96 2
< 0.1%
0.959 2
< 0.1%
0.956 1
< 0.1%
0.955 1
< 0.1%
0.954 1
< 0.1%
0.953 1
< 0.1%
0.952 1
< 0.1%

Acousticness
Real number (ℝ)

High correlation 

Distinct3130
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29141923
Minimum1.11 × 10-6
Maximum0.996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size837.8 KiB
2025-03-11T03:43:58.752708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.11 × 10-6
5-th percentile0.00161
Q10.0452
median0.193
Q30.477
95-th percentile0.885
Maximum0.996
Range0.99599889
Interquartile range (IQR)0.4318

Descriptive statistics

Standard deviation0.28611654
Coefficient of variation (CV)0.98180391
Kurtosis-0.37907104
Mean0.29141923
Median Absolute Deviation (MAD)0.1739
Skewness0.88384744
Sum6000.9048
Variance0.081862674
MonotonicityNot monotonic
2025-03-11T03:43:58.969296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.114 50
 
0.2%
0.161 49
 
0.2%
0.117 45
 
0.2%
0.105 42
 
0.2%
0.118 41
 
0.2%
0.181 40
 
0.2%
0.121 40
 
0.2%
0.173 39
 
0.2%
0.191 37
 
0.2%
0.112 36
 
0.2%
Other values (3120) 20173
98.0%
ValueCountFrequency (%)
1.11 × 10-61
< 0.1%
1.39 × 10-61
< 0.1%
1.77 × 10-61
< 0.1%
2.33 × 10-61
< 0.1%
2.6 × 10-61
< 0.1%
3.19 × 10-61
< 0.1%
3.88 × 10-61
< 0.1%
4 × 10-61
< 0.1%
4.2 × 10-61
< 0.1%
4.21 × 10-61
< 0.1%
ValueCountFrequency (%)
0.996 19
0.1%
0.995 27
0.1%
0.994 27
0.1%
0.993 21
0.1%
0.992 20
0.1%
0.991 18
0.1%
0.99 13
0.1%
0.989 17
0.1%
0.988 16
0.1%
0.987 16
0.1%

Instrumentalness
Real number (ℝ)

Zeros 

Distinct4005
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.056167319
Minimum0
Maximum1
Zeros9317
Zeros (%)45.2%
Negative0
Negative (%)0.0%
Memory size837.8 KiB
2025-03-11T03:43:59.179254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.425 × 10-6
Q30.000474
95-th percentile0.582
Maximum1
Range1
Interquartile range (IQR)0.000474

Descriptive statistics

Standard deviation0.19363013
Coefficient of variation (CV)3.4473807
Kurtosis12.60429
Mean0.056167319
Median Absolute Deviation (MAD)2.425 × 10-6
Skewness3.7125804
Sum1156.5974
Variance0.037492628
MonotonicityNot monotonic
2025-03-11T03:43:59.847380image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9317
45.2%
1.4 × 10-517
 
0.1%
0.00106 16
 
0.1%
1.34 × 10-616
 
0.1%
1.02 × 10-615
 
0.1%
1.1 × 10-615
 
0.1%
1.2 × 10-515
 
0.1%
1.16 × 10-515
 
0.1%
1.31 × 10-615
 
0.1%
0.109 15
 
0.1%
Other values (3995) 11136
54.1%
ValueCountFrequency (%)
0 9317
45.2%
1 × 10-68
 
< 0.1%
1.01 × 10-65
 
< 0.1%
1.02 × 10-615
 
0.1%
1.03 × 10-610
 
< 0.1%
1.04 × 10-613
 
0.1%
1.05 × 10-68
 
< 0.1%
1.06 × 10-68
 
< 0.1%
1.07 × 10-610
 
< 0.1%
1.08 × 10-68
 
< 0.1%
ValueCountFrequency (%)
1 8
< 0.1%
0.999 1
 
< 0.1%
0.995 3
 
< 0.1%
0.993 1
 
< 0.1%
0.992 2
 
< 0.1%
0.989 2
 
< 0.1%
0.988 4
< 0.1%
0.986 2
 
< 0.1%
0.985 1
 
< 0.1%
0.983 1
 
< 0.1%

Liveness
Real number (ℝ)

High correlation 

Distinct1536
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19367209
Minimum0.0145
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size837.8 KiB
2025-03-11T03:44:00.055819image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.0145
5-th percentile0.058355
Q10.0941
median0.125
Q30.237
95-th percentile0.574
Maximum1
Range0.9855
Interquartile range (IQR)0.1429

Descriptive statistics

Standard deviation0.16882947
Coefficient of variation (CV)0.87172847
Kurtosis5.8305648
Mean0.19367209
Median Absolute Deviation (MAD)0.0454
Skewness2.3080089
Sum3988.0956
Variance0.02850339
MonotonicityNot monotonic
2025-03-11T03:44:00.296879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.11 234
 
1.1%
0.109 225
 
1.1%
0.111 214
 
1.0%
0.107 213
 
1.0%
0.108 211
 
1.0%
0.104 196
 
1.0%
0.103 193
 
0.9%
0.105 184
 
0.9%
0.101 183
 
0.9%
0.112 173
 
0.8%
Other values (1526) 18566
90.2%
ValueCountFrequency (%)
0.0145 1
< 0.1%
0.015 1
< 0.1%
0.0157 1
< 0.1%
0.0158 1
< 0.1%
0.0181 1
< 0.1%
0.0182 1
< 0.1%
0.0188 1
< 0.1%
0.019 2
< 0.1%
0.0199 1
< 0.1%
0.02 1
< 0.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
0.997 1
 
< 0.1%
0.99 1
 
< 0.1%
0.986 1
 
< 0.1%
0.984 7
< 0.1%
0.983 5
< 0.1%
0.982 2
 
< 0.1%
0.98 2
 
< 0.1%
0.978 2
 
< 0.1%
0.977 5
< 0.1%

Valence
Real number (ℝ)

Distinct1293
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53012843
Minimum0
Maximum0.993
Zeros26
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size837.8 KiB
2025-03-11T03:44:00.555090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.119
Q10.34
median0.538
Q30.727
95-th percentile0.921
Maximum0.993
Range0.993
Interquartile range (IQR)0.387

Descriptive statistics

Standard deviation0.24549869
Coefficient of variation (CV)0.46309286
Kurtosis-0.92838945
Mean0.53012843
Median Absolute Deviation (MAD)0.194
Skewness-0.10259123
Sum10916.405
Variance0.060269606
MonotonicityNot monotonic
2025-03-11T03:44:00.835931image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.961 71
 
0.3%
0.785 51
 
0.2%
0.962 47
 
0.2%
0.595 44
 
0.2%
0.637 44
 
0.2%
0.491 44
 
0.2%
0.964 43
 
0.2%
0.285 43
 
0.2%
0.284 42
 
0.2%
0.623 41
 
0.2%
Other values (1283) 20122
97.7%
ValueCountFrequency (%)
0 26
0.1%
1 × 10-521
0.1%
0.00237 1
 
< 0.1%
0.00294 1
 
< 0.1%
0.00987 2
 
< 0.1%
0.0129 1
 
< 0.1%
0.0144 1
 
< 0.1%
0.0153 1
 
< 0.1%
0.0154 2
 
< 0.1%
0.0185 3
 
< 0.1%
ValueCountFrequency (%)
0.993 1
 
< 0.1%
0.991 1
 
< 0.1%
0.99 1
 
< 0.1%
0.989 1
 
< 0.1%
0.986 1
 
< 0.1%
0.985 2
< 0.1%
0.984 2
< 0.1%
0.982 2
< 0.1%
0.981 2
< 0.1%
0.98 4
< 0.1%

Tempo
Real number (ℝ)

Distinct14954
Distinct (%)72.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.57433
Minimum0
Maximum243.372
Zeros17
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size837.8 KiB
2025-03-11T03:44:01.134383image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile78.4221
Q196.99475
median119.9595
Q3139.9245
95-th percentile174.72375
Maximum243.372
Range243.372
Interquartile range (IQR)42.92975

Descriptive statistics

Standard deviation29.565662
Coefficient of variation (CV)0.24520694
Kurtosis-0.12441347
Mean120.57433
Median Absolute Deviation (MAD)21.163
Skewness0.39522313
Sum2482866.5
Variance874.12835
MonotonicityNot monotonic
2025-03-11T03:44:01.464597image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77.986 24
 
0.1%
106.002 19
 
0.1%
0 17
 
0.1%
120.031 12
 
0.1%
119.982 11
 
0.1%
100.015 10
 
< 0.1%
140.006 10
 
< 0.1%
120.057 10
 
< 0.1%
129.971 10
 
< 0.1%
106.859 9
 
< 0.1%
Other values (14944) 20460
99.4%
ValueCountFrequency (%)
0 17
0.1%
37.114 1
 
< 0.1%
38.137 1
 
< 0.1%
43.509 1
 
< 0.1%
45.397 1
 
< 0.1%
46.718 1
 
< 0.1%
47.362 3
 
< 0.1%
48.028 1
 
< 0.1%
48.19 2
 
< 0.1%
48.637 1
 
< 0.1%
ValueCountFrequency (%)
243.372 1
< 0.1%
236.059 1
< 0.1%
220.099 1
< 0.1%
215.918 1
< 0.1%
214.025 1
< 0.1%
213.503 1
< 0.1%
211.958 1
< 0.1%
210.857 1
< 0.1%
209.953 1
< 0.1%
209.795 1
< 0.1%

Duration_min
Real number (ℝ)

Skewed 

Distinct14608
Distinct (%)70.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.742802
Minimum0.51641667
Maximum77.9343
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size837.8 KiB
2025-03-11T03:44:01.746320image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.51641667
5-th percentile2.2272667
Q12.9975458
median3.5514333
Q34.2023292
95-th percentile5.5991475
Maximum77.9343
Range77.417883
Interquartile range (IQR)1.2047833

Descriptive statistics

Standard deviation2.084986
Coefficient of variation (CV)0.55706552
Kurtosis785.27539
Mean3.742802
Median Absolute Deviation (MAD)0.5941
Skewness23.346147
Sum77071.779
Variance4.3471664
MonotonicityNot monotonic
2025-03-11T03:44:01.958485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.957783333 26
 
0.1%
3.625783333 19
 
0.1%
3.6 18
 
0.1%
2.666666667 12
 
0.1%
3 12
 
0.1%
4 12
 
0.1%
2.833333333 10
 
< 0.1%
3.2 10
 
< 0.1%
0.516666667 10
 
< 0.1%
4.333333333 9
 
< 0.1%
Other values (14598) 20454
99.3%
ValueCountFrequency (%)
0.516416667 1
 
< 0.1%
0.516666667 10
< 0.1%
0.52395 1
 
< 0.1%
0.583333333 1
 
< 0.1%
0.616666667 1
 
< 0.1%
0.633333333 2
 
< 0.1%
0.65 2
 
< 0.1%
0.666666667 1
 
< 0.1%
0.683333333 1
 
< 0.1%
0.7 1
 
< 0.1%
ValueCountFrequency (%)
77.9343 1
 
< 0.1%
76.35805 9
< 0.1%
68.67096667 1
 
< 0.1%
55.67786667 1
 
< 0.1%
24.73766667 1
 
< 0.1%
23.79965 1
 
< 0.1%
22.16928333 2
 
< 0.1%
16.39053333 1
 
< 0.1%
16.25445 1
 
< 0.1%
15.73875 1
 
< 0.1%

Title
Text

Distinct18022
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
2025-03-11T03:44:02.449357image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length188
Median length80
Mean length48.956051
Min length1

Characters and Unicode

Total characters1008103
Distinct characters161
Distinct categories17 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16471 ?
Unique (%)80.0%

Sample

1st rowGorillaz - Feel Good Inc. (Official Video)
2nd rowGorillaz - Rhinestone Eyes [Storyboard Film] (Official Music Video)
3rd rowGorillaz - New Gold ft. Tame Impala & Bootie Brown (Official Visualiser)
4th rowGorillaz - On Melancholy Hill (Official Video)
5th rowGorillaz - Clint Eastwood (Official Video)
ValueCountFrequency (%)
24155
 
13.4%
video 9320
 
5.2%
official 7464
 
4.2%
music 3097
 
1.7%
the 2646
 
1.5%
ft 1899
 
1.1%
oficial 1409
 
0.8%
feat 1118
 
0.6%
you 934
 
0.5%
me 911
 
0.5%
Other values (20524) 126873
70.6%
2025-03-11T03:44:03.204973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
160152
 
15.9%
i 70708
 
7.0%
e 66945
 
6.6%
a 64040
 
6.4%
o 50382
 
5.0%
n 35611
 
3.5%
l 34342
 
3.4%
r 33956
 
3.4%
s 26685
 
2.6%
t 26646
 
2.6%
Other values (151) 438636
43.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 586388
58.2%
Uppercase Letter 179631
 
17.8%
Space Separator 160162
 
15.9%
Other Punctuation 20141
 
2.0%
Dash Punctuation 18514
 
1.8%
Open Punctuation 15014
 
1.5%
Close Punctuation 15011
 
1.5%
Decimal Number 8459
 
0.8%
Math Symbol 4249
 
0.4%
Currency Symbol 213
 
< 0.1%
Other values (7) 321
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 70708
12.1%
e 66945
11.4%
a 64040
10.9%
o 50382
 
8.6%
n 35611
 
6.1%
l 34342
 
5.9%
r 33956
 
5.8%
s 26685
 
4.6%
t 26646
 
4.5%
d 24502
 
4.2%
Other values (43) 152571
26.0%
Uppercase Letter
ValueCountFrequency (%)
O 14929
 
8.3%
M 13052
 
7.3%
V 12952
 
7.2%
A 12591
 
7.0%
S 12009
 
6.7%
T 10046
 
5.6%
L 9796
 
5.5%
D 8790
 
4.9%
C 8694
 
4.8%
R 7767
 
4.3%
Other values (34) 69005
38.4%
Other Punctuation
ValueCountFrequency (%)
, 5192
25.8%
. 5052
25.1%
? 2812
14.0%
' 1994
 
9.9%
& 1801
 
8.9%
" 1400
 
7.0%
/ 631
 
3.1%
: 589
 
2.9%
! 233
 
1.2%
# 151
 
0.7%
Other values (10) 286
 
1.4%
Decimal Number
ValueCountFrequency (%)
2 1810
21.4%
0 1760
20.8%
1 1597
18.9%
9 671
 
7.9%
4 505
 
6.0%
5 501
 
5.9%
7 448
 
5.3%
3 428
 
5.1%
6 373
 
4.4%
8 366
 
4.3%
Math Symbol
ValueCountFrequency (%)
| 4133
97.3%
+ 79
 
1.9%
~ 30
 
0.7%
× 4
 
0.1%
= 3
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 13050
86.9%
[ 1955
 
13.0%
{ 5
 
< 0.1%
4
 
< 0.1%
Other Symbol
ValueCountFrequency (%)
° 8
38.1%
7
33.3%
® 5
23.8%
¦ 1
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 18262
98.6%
232
 
1.3%
20
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 13050
86.9%
] 1955
 
13.0%
} 6
 
< 0.1%
Final Punctuation
ValueCountFrequency (%)
121
72.0%
44
 
26.2%
» 3
 
1.8%
Initial Punctuation
ValueCountFrequency (%)
44
62.9%
23
32.9%
« 3
 
4.3%
Modifier Symbol
ValueCountFrequency (%)
¨ 8
50.0%
` 5
31.2%
´ 3
 
18.8%
Space Separator
ValueCountFrequency (%)
160152
> 99.9%
  10
 
< 0.1%
Currency Symbol
ValueCountFrequency (%)
$ 213
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 43
100.0%
Other Letter
ValueCountFrequency (%)
º 2
100.0%
Format
ValueCountFrequency (%)
­ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 766021
76.0%
Common 242082
 
24.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 70708
 
9.2%
e 66945
 
8.7%
a 64040
 
8.4%
o 50382
 
6.6%
n 35611
 
4.6%
l 34342
 
4.5%
r 33956
 
4.4%
s 26685
 
3.5%
t 26646
 
3.5%
d 24502
 
3.2%
Other values (88) 332204
43.4%
Common
ValueCountFrequency (%)
160152
66.2%
- 18262
 
7.5%
( 13050
 
5.4%
) 13050
 
5.4%
, 5192
 
2.1%
. 5052
 
2.1%
| 4133
 
1.7%
? 2812
 
1.2%
' 1994
 
0.8%
] 1955
 
0.8%
Other values (53) 16430
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1003835
99.6%
None 3747
 
0.4%
Punctuation 514
 
0.1%
Letterlike Symbols 7
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
160152
 
16.0%
i 70708
 
7.0%
e 66945
 
6.7%
a 64040
 
6.4%
o 50382
 
5.0%
n 35611
 
3.5%
l 34342
 
3.4%
r 33956
 
3.4%
s 26685
 
2.7%
t 26646
 
2.7%
Other values (82) 434368
43.3%
None
ValueCountFrequency (%)
é 608
16.2%
á 470
12.5%
í 464
12.4%
ó 389
10.4%
ã 235
 
6.3%
ñ 171
 
4.6%
ú 161
 
4.3%
ç 154
 
4.1%
ü 152
 
4.1%
ö 138
 
3.7%
Other values (49) 805
21.5%
Punctuation
ValueCountFrequency (%)
232
45.1%
121
23.5%
44
 
8.6%
44
 
8.6%
25
 
4.9%
23
 
4.5%
20
 
3.9%
4
 
0.8%
1
 
0.2%
Letterlike Symbols
ValueCountFrequency (%)
7
100.0%
Distinct6673
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2025-03-11T03:44:03.508791image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length60
Median length45
Mean length12.950903
Min length1

Characters and Unicode

Total characters266685
Distinct characters125
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3897 ?
Unique (%)18.9%

Sample

1st rowGorillaz
2nd rowGorillaz
3rd rowGorillaz
4th rowGorillaz
5th rowGorillaz
ValueCountFrequency (%)
1428
 
4.1%
topic 1066
 
3.0%
music 761
 
2.2%
0 469
 
1.3%
records 386
 
1.1%
the 317
 
0.9%
t-series 249
 
0.7%
official 175
 
0.5%
oficial 137
 
0.4%
tv 132
 
0.4%
Other values (7860) 30062
85.4%
2025-03-11T03:44:04.087633image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 18790
 
7.0%
a 18024
 
6.8%
i 16822
 
6.3%
14628
 
5.5%
o 14581
 
5.5%
V 14457
 
5.4%
n 12712
 
4.8%
r 11776
 
4.4%
s 10259
 
3.8%
l 9266
 
3.5%
Other values (115) 125370
47.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 169232
63.5%
Uppercase Letter 77202
28.9%
Space Separator 14628
 
5.5%
Decimal Number 2711
 
1.0%
Dash Punctuation 1546
 
0.6%
Other Punctuation 1138
 
0.4%
Close Punctuation 69
 
< 0.1%
Open Punctuation 67
 
< 0.1%
Currency Symbol 44
 
< 0.1%
Math Symbol 22
 
< 0.1%
Other values (5) 26
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 18790
11.1%
a 18024
10.7%
i 16822
 
9.9%
o 14581
 
8.6%
n 12712
 
7.5%
r 11776
 
7.0%
s 10259
 
6.1%
l 9266
 
5.5%
t 7504
 
4.4%
c 7197
 
4.3%
Other values (35) 42301
25.0%
Uppercase Letter
ValueCountFrequency (%)
V 14457
18.7%
E 8970
11.6%
O 8761
11.3%
T 4510
 
5.8%
M 4280
 
5.5%
S 4100
 
5.3%
A 3116
 
4.0%
R 3098
 
4.0%
C 2881
 
3.7%
L 2552
 
3.3%
Other values (26) 20477
26.5%
Other Punctuation
ValueCountFrequency (%)
? 555
48.8%
. 266
23.4%
' 116
 
10.2%
& 81
 
7.1%
, 28
 
2.5%
/ 19
 
1.7%
! 18
 
1.6%
" 16
 
1.4%
: 14
 
1.2%
* 9
 
0.8%
Other values (6) 16
 
1.4%
Decimal Number
ValueCountFrequency (%)
0 815
30.1%
1 431
15.9%
2 347
12.8%
4 182
 
6.7%
5 178
 
6.6%
3 167
 
6.2%
7 154
 
5.7%
9 153
 
5.6%
6 150
 
5.5%
8 134
 
4.9%
Open Punctuation
ValueCountFrequency (%)
( 57
85.1%
[ 8
 
11.9%
{ 2
 
3.0%
Close Punctuation
ValueCountFrequency (%)
) 57
82.6%
] 8
 
11.6%
} 4
 
5.8%
Dash Punctuation
ValueCountFrequency (%)
- 1545
99.9%
1
 
0.1%
Math Symbol
ValueCountFrequency (%)
| 20
90.9%
+ 2
 
9.1%
Other Symbol
ValueCountFrequency (%)
1
50.0%
® 1
50.0%
Space Separator
ValueCountFrequency (%)
14628
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 44
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 19
100.0%
Final Punctuation
ValueCountFrequency (%)
3
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 1
100.0%
Other Letter
ValueCountFrequency (%)
ª 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 246435
92.4%
Common 20250
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 18790
 
7.6%
a 18024
 
7.3%
i 16822
 
6.8%
o 14581
 
5.9%
V 14457
 
5.9%
n 12712
 
5.2%
r 11776
 
4.8%
s 10259
 
4.2%
l 9266
 
3.8%
E 8970
 
3.6%
Other values (72) 110778
45.0%
Common
ValueCountFrequency (%)
14628
72.2%
- 1545
 
7.6%
0 815
 
4.0%
? 555
 
2.7%
1 431
 
2.1%
2 347
 
1.7%
. 266
 
1.3%
4 182
 
0.9%
5 178
 
0.9%
3 167
 
0.8%
Other values (33) 1136
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 265937
99.7%
None 738
 
0.3%
Punctuation 9
 
< 0.1%
Letterlike Symbols 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 18790
 
7.1%
a 18024
 
6.8%
i 16822
 
6.3%
14628
 
5.5%
o 14581
 
5.5%
V 14457
 
5.4%
n 12712
 
4.8%
r 11776
 
4.4%
s 10259
 
3.9%
l 9266
 
3.5%
Other values (77) 124622
46.9%
None
ValueCountFrequency (%)
é 159
21.5%
á 90
12.2%
ö 63
 
8.5%
í 63
 
8.5%
ü 60
 
8.1%
ó 41
 
5.6%
ã 36
 
4.9%
ú 35
 
4.7%
ñ 27
 
3.7%
ç 27
 
3.7%
Other values (24) 137
18.6%
Punctuation
ValueCountFrequency (%)
5
55.6%
3
33.3%
1
 
11.1%
Letterlike Symbols
ValueCountFrequency (%)
1
100.0%

Views
Real number (ℝ)

High correlation  Zeros 

Distinct19122
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92045291
Minimum0
Maximum8.0796494 × 109
Zeros469
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size837.8 KiB
2025-03-11T03:44:04.289969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15173.7
Q11478416
median13313480
Q367400608
95-th percentile4.2747035 × 108
Maximum8.0796494 × 109
Range8.0796494 × 109
Interquartile range (IQR)65922192

Descriptive statistics

Standard deviation2.7261463 × 108
Coefficient of variation (CV)2.9617444
Kurtosis151.00957
Mean92045291
Median Absolute Deviation (MAD)13178020
Skewness9.307607
Sum1.8953966 × 1012
Variance7.4318736 × 1016
MonotonicityNot monotonic
2025-03-11T03:44:04.504404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 469
 
2.3%
99348431 10
 
< 0.1%
102948585 10
 
< 0.1%
25883254 10
 
< 0.1%
21912 10
 
< 0.1%
1012205556 10
 
< 0.1%
641407658 10
 
< 0.1%
312226510 10
 
< 0.1%
3020790 10
 
< 0.1%
3877674 10
 
< 0.1%
Other values (19112) 20033
97.3%
ValueCountFrequency (%)
0 469
2.3%
2 1
 
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
15 1
 
< 0.1%
21 2
 
< 0.1%
24 1
 
< 0.1%
26 1
 
< 0.1%
28 1
 
< 0.1%
31 1
 
< 0.1%
ValueCountFrequency (%)
8079649362 1
< 0.1%
8079646911 1
< 0.1%
5908398479 1
< 0.1%
5773798407 1
< 0.1%
5773797147 1
< 0.1%
4898831101 1
< 0.1%
4821016218 1
< 0.1%
4679767471 1
< 0.1%
3817733132 1
< 0.1%
3725748519 1
< 0.1%

Likes
Real number (ℝ)

High correlation  Zeros 

Distinct17829
Distinct (%)86.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean648045.07
Minimum0
Maximum50788652
Zeros556
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size837.8 KiB
2025-03-11T03:44:04.753480image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile183.55
Q117546
median115315.5
Q3500095
95-th percentile2976829.9
Maximum50788652
Range50788652
Interquartile range (IQR)482549

Descriptive statistics

Standard deviation1773725.2
Coefficient of variation (CV)2.7370399
Kurtosis137.81808
Mean648045.07
Median Absolute Deviation (MAD)113020.5
Skewness8.746583
Sum1.3344544 × 1010
Variance3.1461012 × 1012
MonotonicityNot monotonic
2025-03-11T03:44:04.970639image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 556
 
2.7%
5730 14
 
0.1%
12 13
 
0.1%
32 13
 
0.1%
1 12
 
0.1%
256 12
 
0.1%
21 11
 
0.1%
533012 10
 
< 0.1%
6604141 10
 
< 0.1%
449797 10
 
< 0.1%
Other values (17819) 19931
96.8%
ValueCountFrequency (%)
0 556
2.7%
1 12
 
0.1%
2 6
 
< 0.1%
3 2
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
7 3
 
< 0.1%
8 2
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
50788652 1
< 0.1%
50788626 1
< 0.1%
40147674 1
< 0.1%
40147618 1
< 0.1%
35892575 1
< 0.1%
31047780 1
< 0.1%
27588224 1
< 0.1%
27588189 1
< 0.1%
26446178 1
< 0.1%
26399133 1
< 0.1%

Comments
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct10428
Distinct (%)50.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26849.019
Minimum0
Maximum16083138
Zeros1064
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size837.8 KiB
2025-03-11T03:44:05.169036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1406
median3006
Q313739
95-th percentile99319.55
Maximum16083138
Range16083138
Interquartile range (IQR)13333

Descriptive statistics

Standard deviation191184.25
Coefficient of variation (CV)7.1207163
Kurtosis2923.6104
Mean26849.019
Median Absolute Deviation (MAD)2967
Skewness44.157794
Sum5.5287501 × 108
Variance3.6551418 × 1010
MonotonicityNot monotonic
2025-03-11T03:44:05.380669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1064
 
5.2%
1 85
 
0.4%
2 85
 
0.4%
4 62
 
0.3%
7 58
 
0.3%
6 54
 
0.3%
3 51
 
0.2%
5 48
 
0.2%
21 46
 
0.2%
10 40
 
0.2%
Other values (10418) 18999
92.3%
ValueCountFrequency (%)
0 1064
5.2%
1 85
 
0.4%
2 85
 
0.4%
3 51
 
0.2%
4 62
 
0.3%
5 48
 
0.2%
6 54
 
0.3%
7 58
 
0.3%
8 37
 
0.2%
9 34
 
0.2%
ValueCountFrequency (%)
16083138 1
< 0.1%
9131761 1
< 0.1%
6535721 1
< 0.1%
6535719 1
< 0.1%
5331537 1
< 0.1%
5130725 1
< 0.1%
4805805 1
< 0.1%
4252791 2
< 0.1%
3637659 1
< 0.1%
3486944 1
< 0.1%

Licensed
Boolean

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size697.0 KiB
True
20592 
ValueCountFrequency (%)
True 20592
100.0%
2025-03-11T03:44:05.559194image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

official_video
Boolean

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size697.0 KiB
True
20592 
ValueCountFrequency (%)
True 20592
100.0%
2025-03-11T03:44:05.729826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Stream
Real number (ℝ)

High correlation  Zeros 

Distinct18337
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3264507 × 108
Minimum0
Maximum3.3865203 × 109
Zeros576
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size837.8 KiB
2025-03-11T03:44:05.927516image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1161688.3
Q115587569
median47302445
Q31.3433841 × 108
95-th percentile5.6965185 × 108
Maximum3.3865203 × 109
Range3.3865203 × 109
Interquartile range (IQR)1.1875084 × 108

Descriptive statistics

Standard deviation2.4236994 × 108
Coefficient of variation (CV)1.8272066
Kurtosis23.564435
Mean1.3264507 × 108
Median Absolute Deviation (MAD)39397710
Skewness4.1473801
Sum2.7314272 × 1012
Variance5.8743189 × 1016
MonotonicityNot monotonic
2025-03-11T03:44:06.214414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 576
 
2.8%
169769959 24
 
0.1%
89466323 19
 
0.1%
9999321 9
 
< 0.1%
23128326 9
 
< 0.1%
48533462 9
 
< 0.1%
179230151 9
 
< 0.1%
55399812 9
 
< 0.1%
71682526 7
 
< 0.1%
61921081 7
 
< 0.1%
Other values (18327) 19914
96.7%
ValueCountFrequency (%)
0 576
2.8%
6574 1
 
< 0.1%
7771 1
 
< 0.1%
8053 1
 
< 0.1%
8074 1
 
< 0.1%
10306 1
 
< 0.1%
10540 1
 
< 0.1%
10660 1
 
< 0.1%
10701 1
 
< 0.1%
10710 1
 
< 0.1%
ValueCountFrequency (%)
3386520288 1
< 0.1%
3362005201 1
< 0.1%
2634013335 1
< 0.1%
2594926619 1
< 0.1%
2538329799 2
< 0.1%
2522431995 1
< 0.1%
2456205158 2
< 0.1%
2369272335 1
< 0.1%
2365777505 2
< 0.1%
2336219850 2
< 0.1%

EnergyLiveness
Real number (ℝ)

High correlation 

Distinct17433
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1672268
Minimum4.87 × 10-5
Maximum59.113924
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size837.8 KiB
2025-03-11T03:44:06.544755image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum4.87 × 10-5
5-th percentile0.89929967
Q12.3861898
median4.2568807
Q36.8220339
95-th percentile12.512649
Maximum59.113924
Range59.113875
Interquartile range (IQR)4.4358441

Descriptive statistics

Standard deviation4.1174313
Coefficient of variation (CV)0.7968358
Kurtosis16.007782
Mean5.1672268
Median Absolute Deviation (MAD)2.0903955
Skewness2.6922202
Sum106403.53
Variance16.953241
MonotonicityNot monotonic
2025-03-11T03:44:06.751044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.86 24
 
0.1%
6.23015873 19
 
0.1%
3 18
 
0.1%
4 17
 
0.1%
5 17
 
0.1%
6 16
 
0.1%
2 13
 
0.1%
7 13
 
0.1%
4.5 11
 
0.1%
2.5 11
 
0.1%
Other values (17423) 20433
99.2%
ValueCountFrequency (%)
4.87 × 10-51
 
< 0.1%
0.000312655 2
< 0.1%
0.000482456 2
< 0.1%
0.011261261 3
< 0.1%
0.01316092 1
 
< 0.1%
0.014428858 1
 
< 0.1%
0.015818182 1
 
< 0.1%
0.017027027 1
 
< 0.1%
0.020384615 1
 
< 0.1%
0.020629921 1
 
< 0.1%
ValueCountFrequency (%)
59.11392405 1
< 0.1%
58 1
< 0.1%
57.65517241 1
< 0.1%
54.52229299 1
< 0.1%
51.38121547 1
< 0.1%
49.23076923 1
< 0.1%
48.9 1
< 0.1%
45.52763819 1
< 0.1%
43.77659574 1
< 0.1%
42.52380952 1
< 0.1%

most_playedon
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Spotify
15692 
Youtube
4900 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters144144
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSpotify
2nd rowSpotify
3rd rowSpotify
4th rowSpotify
5th rowYoutube

Common Values

ValueCountFrequency (%)
Spotify 15692
76.2%
Youtube 4900
 
23.8%

Length

2025-03-11T03:44:06.938334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T03:44:07.083756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
spotify 15692
76.2%
youtube 4900
 
23.8%

Most occurring characters

ValueCountFrequency (%)
o 20592
14.3%
t 20592
14.3%
S 15692
10.9%
p 15692
10.9%
i 15692
10.9%
f 15692
10.9%
y 15692
10.9%
u 9800
6.8%
Y 4900
 
3.4%
b 4900
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 123552
85.7%
Uppercase Letter 20592
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 20592
16.7%
t 20592
16.7%
p 15692
12.7%
i 15692
12.7%
f 15692
12.7%
y 15692
12.7%
u 9800
7.9%
b 4900
 
4.0%
e 4900
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
S 15692
76.2%
Y 4900
 
23.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 144144
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 20592
14.3%
t 20592
14.3%
S 15692
10.9%
p 15692
10.9%
i 15692
10.9%
f 15692
10.9%
y 15692
10.9%
u 9800
6.8%
Y 4900
 
3.4%
b 4900
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 144144
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 20592
14.3%
t 20592
14.3%
S 15692
10.9%
p 15692
10.9%
i 15692
10.9%
f 15692
10.9%
y 15692
10.9%
u 9800
6.8%
Y 4900
 
3.4%
b 4900
 
3.4%

Interactions

2025-03-11T03:43:50.208557image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:17.290169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:19.995987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:22.769807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:24.883765image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:27.422267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:29.809365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:31.905944image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:34.170993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:36.303652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:38.765606image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:41.107878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:43.249921image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:45.396151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:47.988118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:50.347484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:17.492365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:20.163933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:22.900109image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:25.013991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:27.566067image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:29.938751image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:32.059402image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:34.315375image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:36.433707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:38.914882image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:41.247804image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:43.401710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:45.536090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:48.133856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:50.497889image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:17.680954image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:20.314211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:23.038228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:25.146026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:27.711623image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:30.064856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:32.196229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:34.458686image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:36.563724image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:39.068119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:41.374062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:43.545997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:45.683829image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:48.282173image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:50.648586image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:17.878953image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:20.487852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:23.177208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:25.271536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:27.853550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:30.196065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:32.335984image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:34.583532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:36.993040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:39.222127image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:41.488000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:43.681164image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:45.834270image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:48.421710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:50.781293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:18.041337image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:20.663920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:23.327557image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:25.387003image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:27.980205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:30.337549image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:32.488078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:34.714825image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:37.138080image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:39.382504image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:41.622267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:43.823547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:45.970195image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:48.608931image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:50.912313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:18.228891image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:20.808301image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:23.462411image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:25.497096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:28.115203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:30.491151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:32.648525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:34.853944image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:37.307973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:39.521122image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:41.777052image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:43.956989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:46.494197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:48.746151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:51.047187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:18.403875image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:20.954730image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:23.593947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:25.671944image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:28.244560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:30.622172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:32.831058image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:35.020589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:37.442119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:39.692767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:41.916966image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:44.092189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:46.641508image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:48.876580image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:51.178769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:18.556851image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:21.100235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:23.723806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:25.856899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:28.371289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:30.782242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:32.995662image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:35.152401image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:37.582064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:39.866030image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:42.076865image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:44.240103image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:46.783934image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:49.023217image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:51.327713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:18.736767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:21.327980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:23.854999image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:26.040427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:28.499980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:30.911151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:33.130046image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:35.290068image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:37.725141image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:40.011009image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:42.221263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:44.379957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:46.920490image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:49.159683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:51.523628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:18.893821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:21.507926image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:23.984082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:26.228924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:28.627310image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:31.038967image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:33.286564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:35.425969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:37.855045image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:40.157115image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:42.360868image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:44.526104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:47.058151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:49.295977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:51.675985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:19.088427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:21.693952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:24.123603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:26.452437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:29.019121image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:31.184886image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:33.439130image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:35.584626image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:37.996765image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:40.305999image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:42.512256image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:44.666129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:47.214937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:49.451753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:51.834682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:19.260747image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:21.886344image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:24.264543image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:26.699472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:29.170008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:31.338422image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:33.580112image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:35.745454image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:38.142280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:40.497889image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:42.640918image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:44.808900image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:47.370373image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:49.623877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:51.996734image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:19.422512image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:22.247868image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:24.402166image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:26.886668image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:29.304869image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:31.467332image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:33.728025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:35.880662image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:38.280403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:40.648955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:42.764973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:44.942030image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:47.528173image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:49.763520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:52.177039image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:19.612683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:22.447613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:24.592756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:27.104020image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:29.470240image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:31.619923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:33.896354image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:36.030574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:38.461697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:40.812034image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:42.928437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:45.095059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:47.683553image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:49.923503image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:52.389895image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:19.852581image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:22.627260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:24.740072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:27.275503image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:29.650736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:31.767867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:34.033942image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:36.168149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:38.620524image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:40.963636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:43.091078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:45.262882image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:47.842536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-11T03:43:50.069219image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-03-11T03:44:07.212181image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
AcousticnessAlbum_typeCommentsDanceabilityDuration_minEnergyEnergyLivenessInstrumentalnessLikesLivenessLoudnessSpeechinessStreamTempoValenceViewsmost_playedon
Acousticness1.0000.052-0.174-0.140-0.062-0.571-0.308-0.000-0.138-0.042-0.435-0.147-0.105-0.137-0.105-0.1300.104
Album_type0.0521.0000.0000.1160.0830.0750.0280.0330.0080.0290.1100.0700.0510.0470.0470.0160.093
Comments-0.1740.0001.0000.1150.1660.1430.136-0.1490.943-0.0170.2400.0610.5250.0380.0190.9110.055
Danceability-0.1400.1160.1151.000-0.1380.1200.246-0.1490.153-0.1090.1980.3130.057-0.0680.4320.1330.087
Duration_min-0.0620.0830.166-0.1381.0000.0190.0490.0640.124-0.020-0.046-0.1580.077-0.023-0.0930.1720.023
Energy-0.5710.0750.1430.1200.0191.0000.373-0.0730.1180.1410.7100.2350.0410.1450.3450.1290.092
EnergyLiveness-0.3080.0280.1360.2460.0490.3731.000-0.0490.137-0.7890.3250.0870.0820.0790.2380.1360.028
Instrumentalness-0.0000.033-0.149-0.1490.064-0.073-0.0491.000-0.167-0.087-0.292-0.197-0.119-0.021-0.161-0.1650.095
Likes-0.1380.0080.9430.1530.1240.1180.137-0.1671.000-0.0270.2430.0810.5680.0380.0290.9610.148
Liveness-0.0420.029-0.017-0.109-0.0200.141-0.789-0.087-0.0271.0000.0820.052-0.0340.007-0.018-0.0230.054
Loudness-0.4350.1100.2400.198-0.0460.7100.325-0.2920.2430.0821.0000.1860.1470.1160.2420.2360.099
Speechiness-0.1470.0700.0610.313-0.1580.2350.087-0.1970.0810.0520.1861.000-0.0170.0700.1280.0230.051
Stream-0.1050.0510.5250.0570.0770.0410.082-0.1190.568-0.0340.147-0.0171.0000.027-0.0090.5670.042
Tempo-0.1370.0470.038-0.068-0.0230.1450.079-0.0210.0380.0070.1160.0700.0271.0000.0730.0380.025
Valence-0.1050.0470.0190.432-0.0930.3450.238-0.1610.029-0.0180.2420.128-0.0090.0731.0000.0720.088
Views-0.1300.0160.9110.1330.1720.1290.136-0.1650.961-0.0230.2360.0230.5670.0380.0721.0000.192
most_playedon0.1040.0930.0550.0870.0230.0920.0280.0950.1480.0540.0990.0510.0420.0250.0880.1921.000

Missing values

2025-03-11T03:43:52.673809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-11T03:43:53.196971image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ArtistTrackAlbumAlbum_typeDanceabilityEnergyLoudnessSpeechinessAcousticnessInstrumentalnessLivenessValenceTempoDuration_minTitleChannelViewsLikesCommentsLicensedofficial_videoStreamEnergyLivenessmost_playedon
0GorillazFeel Good Inc.Demon Daysalbum11-7000111394Gorillaz - Feel Good Inc. (Official Video)Gorillaz6935552216220896169907TrueTrue10402348541Spotify
1GorillazRhinestone EyesPlastic Beachalbum11-600001933Gorillaz - Rhinestone Eyes [Storyboard Film] (Official Music Video)Gorillaz72011645107912831003TrueTrue31008373315Spotify
2GorillazNew Gold (feat. Tame Impala and Bootie Brown)New Gold (feat. Tame Impala and Bootie Brown)single11-4000011084Gorillaz - New Gold ft. Tame Impala & Bootie Brown (Official Visualiser)Gorillaz84350552821427399TrueTrue630634678Spotify
3GorillazOn Melancholy HillPlastic Beachalbum11-6001011204Gorillaz - On Melancholy Hill (Official Video)Gorillaz211754952178857755229TrueTrue43466355912Spotify
4GorillazClint EastwoodGorillazalbum11-9000011686Gorillaz - Clint Eastwood (Official Video)Gorillaz6184809586197318155930TrueTrue61725973810Youtube
5GorillazDAREDemon Daysalbum11-6000011204Gorillaz - DARE (Official Video)Gorillaz259021161184465872008TrueTrue3238503273Spotify
6GorillazNew Gold (feat. Tame Impala and Bootie Brown) - Dom Dolla RemixNew Gold (feat. Tame Impala and Bootie Brown) [Dom Dolla Remix]single11-7000001275Gorillaz - New Gold ft. Tame Impala, Bootie Brown (Dom Dolla Remix) (Official Live Video)Dom Dolla45199611686241TrueTrue106661543Spotify
7GorillazShe's My Collar (feat. Kali Uchis)Humanz (Deluxe)album11-6000001403Gorillaz - She's My Collar [HQ]SalvaMuñox101098217675260TrueTrue1596059297Spotify
8GorillazCracker Island (feat. Thundercat)Cracker Island (feat. Thundercat)single11-3000011204Gorillaz - Cracker Island ft. Thundercat (Official Video)Gorillaz2445982073952720296TrueTrue426719013Spotify
9GorillazDirty HarryDemon Daysalbum11-7000111924Gorillaz - Dirty Harry (Official Video)Gorillaz154761056138692039240TrueTrue1910747131Spotify
ArtistTrackAlbumAlbum_typeDanceabilityEnergyLoudnessSpeechinessAcousticnessInstrumentalnessLivenessValenceTempoDuration_minTitleChannelViewsLikesCommentsLicensedofficial_videoStreamEnergyLivenessmost_playedon
20584SICK LEGENDPART OF ME HARDSTYLE (SPED UP)PART OF ME HARDSTYLE (SPED UP)single11-3000011022PART OF ME HARDSTYLE (SPED UP)SICK LEGEND - Topic408146400TrueTrue177215882Spotify
20585SICK LEGENDSUMMER TIME SADNESS HARDSTYLESUMMER TIME SADNESS HARDSTYLEsingle01-6000001672SUMMER TIME SADNESS HARDSTYLESICK LEGEND - Topic237193620TrueTrue108382542Spotify
20586SICK LEGENDPART OF ME HARDSTYLEPART OF ME HARDSTYLEsingle11-3000011752PART OF ME HARDSTYLESICK LEGEND - Topic37071146390TrueTrue163321333Spotify
20587SICK LEGENDMIDDLE OF THE NIGHT - HARDSTYLE REMIXMIDDLE OF THE NIGHT - HARDSTYLE REMIXsingle01-7000001853MIDDLE OF THE NIGHT - HARDSTYLE REMIXSICK LEGEND - Topic25426834720TrueTrue171251772Spotify
20588SICK LEGENDEVERYTIME WE TOUCH HARDSTYLE (SPED UP)EVERYTIME WE TOUCH HARDSTYLE (SPED UP)single11-5000011022EVERYTIME WE TOUCH HARDSTYLE (SPED UP)SICK LEGEND - Topic160042670TrueTrue99218873Spotify
20589SICK LEGENDJUST DANCE HARDSTYLEJUST DANCE HARDSTYLEsingle11-600001902JUST DANCE HARDSTYLESICK LEGEND - Topic7167811130TrueTrue922714411Spotify
20590SICK LEGENDSET FIRE TO THE RAIN HARDSTYLESET FIRE TO THE RAIN HARDSTYLEsingle11-2000011753SET FIRE TO THE RAIN HARDSTYLESICK LEGEND - Topic16474120190TrueTrue1089817610Spotify
20591SICK LEGENDOUTSIDE HARDSTYLE SPED UPOUTSIDE HARDSTYLE SPED UPsingle01-5000001682OUTSIDE HARDSTYLE SPED UPSICK LEGEND - Topic356463290TrueTrue62261105Spotify
20592SICK LEGENDONLY GIRL HARDSTYLEONLY GIRL HARDSTYLEsingle01-4000011552ONLY GIRL HARDSTYLESICK LEGEND - Topic6533880TrueTrue68739617Spotify
20593SICK LEGENDMISS YOU HARDSTYLEMISS YOU HARDSTYLEsingle01-5001001603MISS YOU HARDSTYLESICK LEGEND - Topic15869724840TrueTrue56955847Spotify